EP3894881A1 - Procédé de suppression d'interférences et procédés de rétablissement du signal - Google Patents

Procédé de suppression d'interférences et procédés de rétablissement du signal

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Publication number
EP3894881A1
EP3894881A1 EP19817657.0A EP19817657A EP3894881A1 EP 3894881 A1 EP3894881 A1 EP 3894881A1 EP 19817657 A EP19817657 A EP 19817657A EP 3894881 A1 EP3894881 A1 EP 3894881A1
Authority
EP
European Patent Office
Prior art keywords
spectrum
interference
time signal
determined
signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP19817657.0A
Other languages
German (de)
English (en)
Inventor
Jonathan Bechter
Florian Engels
Mouhammad Alhumaidi
Amarilda Demirlika
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ZF Friedrichshafen AG
Original Assignee
ZF Friedrichshafen AG
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Filing date
Publication date
Application filed by ZF Friedrichshafen AG filed Critical ZF Friedrichshafen AG
Publication of EP3894881A1 publication Critical patent/EP3894881A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/356—Receivers involving particularities of FFT processing

Definitions

  • the invention relates to a method for interference suppression and a method for signal restoration.
  • Such a method is described by “Marvasti et al: Sparse Signal Processing Using iterative Method with Adaptive Threshold (IMAT)”.
  • a time signal is initially provided, in which partial areas of the time signal are unknown.
  • the values within these partial ranges are zeroed in order to provide a modified time signal, which is then converted into a frequency spectrum using a Fourier transformation.
  • Due to the zeroed sub-areas corresponding interference components occur in the frequency spectrum, which are also called side lobes or side lobes.
  • Such side lobes are distributed around real frequency components, but have a lower amplitude than the real frequency component. Accordingly, it can be said with certainty that the frequency components with the highest amplitude within the spectrum are real frequency components.
  • the IMAT process requires a large number of Fourier transformations, which are particularly memory and computation intensive. Because of this, this procedure Particularly in the case of mobile applications, that is to say real-time evaluations of radar signals in mobile objects, can be implemented only at great expense. This is particularly the case in automotive applications that provide driver assistance functions or autonomous driving functions. FMCW radars (Frequency Modulated Continuous Wave) are often used here, in which Fourier transformations are necessary for the resolution of detections in distance, speed, azimuth angle and elevation angle.
  • the method is particularly suitable for use in areas in which computing capacity can only be kept available at correspondingly high costs.
  • the method is particularly suitable for radar systems which are formed in mobile objects. These can be, for example, radar systems for motor vehicles or other autonomous vehicles.
  • Such a radar system can be designed, for example, as a frequency modulated continuous wave radar, FMCW radar.
  • FMCW radars The functionality of FMCW radars is well known.
  • this emits radar waves in the form of a plurality of successive chirps or frequency ramps which are reflected on objects within the field of view, the reflected radar waves being detected by the receiving antennas.
  • the detected signals are then evaluated using a number of Fourier transformations.
  • the Fourier transformations can be used to resolve the information of the time signals in relation to the distance, the speed, and an elevation angle and an azimuth angle for each detection.
  • the method described below can only be used for a single or for several of the above named dimensions are applied. Insofar as examples are given below, for the sake of simplicity, they only relate to the use in a single dimension in order to enable a simple understanding of the functioning of the method.
  • the extension of the method to another dimension or to further dimensions is carried out analogously.
  • a time signal is provided in a first step a.
  • a time signal can be, for example, a measurement signal from a RADAR system or a time signal from another source.
  • the time signal is provided as a digital signal.
  • an analog measurement signal can be converted into a digital measurement signal using an analog-digital converter.
  • a digital time signal has a plurality of touch points, which result from the sampling of an analog signal.
  • an interference range of the time signal is determined.
  • the interference area has disturbances in the actual time signal. Such a disturbance can have different causes. In the case of RADAR systems, this can arise, for example, from RADAR signals which are emitted by another RADAR system. Due to the interference within the interference range, the time signal in this area cannot be used for further processing, since due to the interference components within the frequency spectrum, no reliable information can be given about the actual detections and interference detections that result from interference, of the radar.
  • interference areas for example Barjenbruch et al: A method for interference cancellation in automotive radar, 2015 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), 2015 or Fischer et al: Robust detection and mitigation of mutual interference in automotive radar, 2015 16th International Radar Symposium (IRS), 2015, 143-148.
  • ICMIM International Conference on Microwaves for Intelligent Mobility
  • Fischer et al Robust detection and mitigation of mutual interference in automotive radar, 2015 16th International Radar Symposium (IRS), 2015, 143-148.
  • These interference areas in particular their interference locations, that is to say the associated touch points of the time signal, are stored in order to keep this information ready for further processing.
  • This information can be stored in an interference matrix, for example.
  • the interference areas can be described mathematically, for example, as a window function.
  • the window function is selected, for example, in such a way that the touch points within the disturbed area are set to zero, the touch points outside the interference areas keeping their values unchanged.
  • a modified time signal is provided by erasing the interference area of the time signal.
  • the interference areas are advantageously erased by setting the interference areas to zero.
  • the touch points of the time signal that are affected by the fault are zeroed.
  • the touch points affected by the fault are also referred to as interference points.
  • the modified time signal thus includes the original time signal with its touch points as well as the zeroed interference points.
  • a spectrum of the modified time signal is determined and a redemption spectrum of the canceled interference area is determined.
  • the spectrum is determined using a Fourier transform from the modified time signal.
  • the tilt spectrum is determined from the knowledge of the interference areas, for example by Fourier transformation of the window function. Due to the erased interference areas, a spectrum results which differs from the spectrum of the undisturbed signal. These differences can be remedied with knowledge of the redemption spectrum.
  • Such a redemption spectrum corresponds to the course of interference components, so-called side lobes, these interference components depending on the interference regions, in particular the window function. These interference components are distributed around every real frequency that forms a component of the time signal.
  • the amplitude of the interference components in the spectrum depends on the amplitude of the real frequency by which the interference components are distributed. However, the interference components have a smaller amplitude than the associated frequency. In particular, the distribution of the interference components in the spectrum also depends on the phase, the frequency and the amplitude of the real frequency. Since the interference components always have a lower amplitude than the frequency components around which the side lobes are arranged, one can distinguish actual frequency components from the interference components.
  • real frequencies or real frequency components of the spectrum are determined. Such a real frequency component is a frequency component that is known with certainty to be an actual frequency component within the present time signal. The real frequency component is therefore not based on interference components that are introduced into the spectrum by the canceled interference areas.
  • the frequency components and their amplitude around which the interference components are arranged are always larger than the interference components.
  • These real frequencies or frequency components can, for example, be determined using a limit value. It is also possible to determine a total maxima as well as local maxima. With regard to the frequency components determined, it can be said with certainty that these are real frequency components of the time signal.
  • a characteristic variable preferably a plurality of characteristic variables, is determined with respect to the real frequency. Characteristic quantities are in particular the amplitude, the phase and / or the frequency of the real part of the frequency.
  • a single real frequency or a plurality of real frequencies can be determined in step e.
  • the term real frequency component does not refer to a real part or an imaginary part but to whether a frequency is actually present in the time signal or is generated by the window function as an interference frequency.
  • a modified redemption spectrum is determined from a characteristic variable of the safe frequency from the redemption spectrum.
  • the characteristic quantities are determined when determining the real frequencies or frequency components.
  • the redemption spectrum is also known.
  • the repayment spectrum can be adjusted based on the characteristic size or sizes to the real frequency components.
  • the redemption spectrum shifted to the real frequency and adapted to the amplitude and phase of the real frequency, which results in the modified redemption spectrum.
  • the modified redemption spectrum corresponds to the distribution of the interference components around the determined real frequency. By adapting the phase, frequency and amplitude, the course of the redemption spectrum can be determined very precisely.
  • a step g the modified eradication spectrum is subtracted from the spectrum in order to provide a corrected spectrum.
  • This removes the interference components that were introduced by the real frequencies from the spectrum.
  • the real frequency components and their characteristic quantities are determined and, knowing the course of the interference components on the basis of the eradication spectrum, these interference components are removed from the spectrum.
  • the corrected spectrum is thus adjusted for the interference components of the real frequencies. If several real frequencies are determined according to step e, the interference components of all determined real frequencies can be removed in step g.
  • step d In comparison to the IMAT, which was briefly described in the introduction, only a single Fourier transformation is carried out here in step d. With regard to the IMAT method, three Fourier transformations are already being carried out for the provision of a first corrected spectrum. In addition, the computing operations of the presented method are much easier to calculate and thus save computing capacity.
  • Steps e to g are particularly advantageously carried out on the corrected spectrum for a further real frequency component.
  • the corrected spectrum is used as the starting point for the determination of further real frequencies or real frequency components.
  • real frequency components already determined after step e are no longer taken into account, since their interference components have already been removed in accordance with step g.
  • the further steps f and g are then carried out with the newly determined real frequencies.
  • the modified redemption spectrum can be determined and the subtraction can be carried out from the spectrum used in step e.
  • This provides a further corrected spectrum.
  • the corrected spectrum provided after the first run corresponds to a first quality level, the corrected spectrum corresponding to the second run corresponding to a second quality level, etc.
  • steps e to g can be repeated several times.
  • further real frequency components may occur within the Spectrum that was previously overlaid by the interference.
  • the iterative process restores the spectrum of undisturbed time signals step by step.
  • the method for signal restoration according to claim 3 comprises at least steps a to e of claim 1 or the preceding explanations.
  • the procedure is identical at least for these points.
  • the real frequencies determined are used for signal restoration in order to restore the interference areas.
  • the frequency, the phase and the amplitude of the real frequency or the real frequencies can be read from the spectrum.
  • the actual time signal can be restored within the interference ranges. This enables simple signal recovery.
  • steps f to g and the subsequent determination of further real frequency components can also be carried out.
  • the number of real frequency components determined is higher, so that an improved restoration of the time signal is possible.
  • steps e to g according to claim 2 can also be carried out several times in order to increase the number of determined real frequencies and thereby improve the restoration of the time signal within the interference ranges.
  • steps a to g of the method according to claim 1 are carried out for signal recovery, steps e to g being able to be carried out several times.
  • the safe frequencies are then determined in order to restore the interference areas of the time signal.
  • the corresponding frequencies and their amplitude, frequency and phase are added up.
  • the time signal can be restored within the interference areas. In particular, this provides a restored time signal that includes the original time signal with the restored interference areas.
  • the signal restoration is much more efficient than using the IMAT method, since only a single Fourier transformation is carried out here.
  • Another method for signal restoration comprises at least steps a to g of claim 1.
  • the corrected spectrum is then used to determine the time signal. This can be done, for example, by an inverse Fourier transformation. This provides a complete and restored time signal from the corrected spectrum.
  • steps e to g of the method according to claim 1 as explained in claim 2 are carried out several times.
  • This provides a high-quality spectrum, which then provides a re-established time signal with the aid of the inverse Fourier transformation.
  • this method only two Fourier transforms are necessary for signal recovery.
  • the described method for signal restoration is particularly efficient compared to the IMAT method.
  • Each Fourier transform resolves another dimension. For example, in a first step the position was resolved, in a second step the speed, in a third and fourth step the azimuth angle and the elevation angle.
  • This provides a multi-dimensional space within which the individual radar detections can be determined.
  • Various sizes are determined in the spectrum, such as phase, amplitude, frequency, etc., in order to infer the distance, speed, azimuth angle or elevation angle in relation to the radar.
  • the signal-to-noise ratio increases with each further resolved dimension.
  • the resolved dimensions include distance, speed, elevation angle and / or azimuth angle.
  • the multiple dimension at least two, three or four dimensions are resolved. For example, the distance and speed of the detections.
  • the IMAT procedure for providing a corrected spectrum of the first quality level for 4 dimensions must first carry out 8 Fourier transformations. 4 Fourier transformations to determine the 4 dimensional spectrum and then 4 inverse Fourier transformations to determine the corrected time signal. In addition, 4 Fourier transformations are necessary in order to determine the corrected spectrum from the corrected time signal. In total there are 12 Fourier transformations and 8 more for each additional quality level. With regard to the proposed method, only four Fourier transformations are necessary, since the correction is carried out directly in the spectrum. In addition, the Fourier transformations are processed with a conventional FMCW radar anyway, in order to obtain the spectrum.
  • the characteristic size of the real frequency component is advantageously determined with high accuracy.
  • the precise determination of the characteristic variables such as frequency, amplitude and / or phase is of particular advantage.
  • the modified Til spectrum is provided precisely. This removes the interference components from the spectrum. If the characteristic quantities are determined inaccurately, then it is possible that the interference components are not removed correctly and an incorrectly corrected spectrum is generated as a result.
  • Such a window function includes at least the interference area. At least these are all interference points. Conveniently, at least all interference points are set to the value zero.
  • the window function is applied to the time signal, thereby eliminating the interference area.
  • the window function can also affect areas of the time signal that have no interference components, i.e. touch points that are not interference points. For example, the window function extends somewhat beyond the width of the interference areas. This makes it possible to ensure that no marginal areas of the disturbance remain within the modified time signal.
  • the window function can also be formed by another function, in particular a smoothed rectangular function. Such a smoothed rectangular function includes the rectangular function, which does not jump from 0 to 1 or vice versa, but falls off smoothly.
  • the area of the transition is referred to as the transition portion and can be provided, for example, by a cos 2 or sin 2 function.
  • the transition portion of the smoothed rectangular function for example, reduces the amplitude of the part of the time signal which is not affected by the disturbance, but is on an edge region of the disturbance.
  • the rectangular portion of the smoothed rectangular function preferably covers the entire interference area, the transition portion being applied to undisturbed areas or edge areas of the undisturbed time signal.
  • some of the touch points of the time signal are reduced in their amplitude, thereby providing that the interference components of the redemption spectrum have a smaller amplitude in relation to the assigned real frequency and also decrease more rapidly as the relative frequency increases.
  • the number of real frequency components that can be determined in a first pass is relatively larger than when using a purely rectangular function.
  • the window function is advantageously formed by a rectangular function or also a smoothed function.
  • the smoothed rectangular function preferably also acts on a time signal area which is adjacent to an interference area.
  • the methods are used in particular in a RADAR system to determine targets with high accuracy even in the event of interference to the received signals.
  • it can also be used to record targets that could not be determined due to the disruptions.
  • the process is so efficient that it can even be carried out in real time with the low-performance hardware of a motor vehicle.
  • 1 shows a time signal
  • 2 shows an undisturbed spectrum, a spectrum and a corrected spectrum of the time signal
  • FIG. 5 flow chart of a method for interference suppression
  • a time signal 10 is shown by a solid line.
  • the time signal 10 will be provided by a RADAR system. In this case it is the beat signal of a chirp of an FCMW radar.
  • the transmitted, reflected on an object and received again was mixed with the output signal of the transmitting antenna and filtered via a low pass.
  • the mixed signal was accordingly converted into a digital signal which has the key points 0 to 512.
  • the individual touch points are plotted along the X axis 12, the amplitude of the digital signal being plotted against the Y axis 13. It can be seen that there is a signal disturbance 14 for the touch points 250 to 280. This area is also referred to as the interference area 16.
  • Such a signal disturbance 14 can be triggered, for example, by extraneous radiation that was received together with the reflected signal.
  • the individual steps of the method for signal recovery and interference suppression explained below are shown as a flowchart in FIG. 5 and are explained in detail below.
  • the time signal is provided as the first step a.
  • a further step b it is determined whether the time signal 10 has a signal disturbance 14. This can be done, for example, with the aid of an algorithm that determines the interference area 16 of the time signal 10.
  • the interference area 16 comprises the interference points which comprise the touch points of the time signal 10 at which the signal disturbance 14 occurs.
  • the algorithm has determined the touch points 250 to 280 as interference points for the time signal 10.
  • the interference area or the interference points are stored for the further method. This can be done, for example, in the form of an interference matrix.
  • a step c all touch points of the time signal 10 within the interference area 16 are erased by setting them to the value 0.
  • a modified time signal 18 is thereby provided.
  • the modified time signal 18 corresponds to the time signal 10, the dashed line representing the course of the modified time signal within the interference region 16.
  • the amplitude of the time signal within the interference range 16 is set to the value zero for the associated touch points, that is to say the interference points.
  • a spectrum and a redemption spectrum are determined.
  • the spectrum results from the modified time signal 18. This can be calculated, for example, by a Fourier transformation.
  • the spectrum 20 which results from the modified time signal 18 is shown in FIG. 2 by the dash-dotted line.
  • FIG. 2 shows the actual spectrum 22, which corresponds to the undisturbed time signal, by a solid line.
  • the actual spectrum 22 thus corresponds to the comparison case in that no interference has occurred. 2
  • the frequency is plotted against the X axis 24 and the amplitude is plotted against the Y axis 26.
  • the repayment spectrum 28 is shown by way of example in FIG. 3. Compared to the X-axis 27a, the frequency and against the Y-axis 27b, the amplitude is shown.
  • This amortization spectrum results from the window function that was used for the amalgamation of the interference area 16 of the time signal 10 in step C.
  • This window function is selected as a rectangular function which sets the time signal 10 to zero for the interference points.
  • the window function is shown as an example in FIG. 4a).
  • the modified time signal can be described mathematically for an individual chirp of an FMCW radar as follows.
  • a chirp duration of T c is assumed, the interference occurring at time t 0 and having a duration of T Int .
  • the first summand describes the frequency components
  • the second summand the window function.
  • the rectangular functions rect are chosen so that the amplitude within the interference range is set to zero.
  • a second si function appears here, which is folded with the target frequency / 0 .
  • the si function is weighted with the amplitude A Q and the phase f 0 . / 0 ) is the Dirac function, which is zero at every position, except for f - f 0 .
  • the interference components 28a of the repayment function are shown in FIG. 3.
  • the redemption spectrum results from the Fourier transform of the interference matrix or from the window function used.
  • the repayment spectrum which ches is determined using the Fourier transform, it is known how corresponding interference components 28a, which are introduced by the window function, arrange around the target frequency and, accordingly, around the real frequency components.
  • the spectrum and the redemption spectrum are now known. It is also known that the interference components 28a have a smaller amplitude than the associated real frequency components.
  • the real frequency components of the spectrum are determined. This can be done, for example, with the help of a limit value analysis. The highest value of the spectrum is determined and a limit value is set. All frequency components that are above this limit can be considered as real frequency components. Alternatively, you can also search for absolute maxima or local maxima, where the local maxima must represent a maximum over a sufficient spectral frequency range.
  • the spectrum 22 is shown, which detects two targets in relation to a RADAR measurement of an FMCWs RADAR.
  • the spectrum 22 comprises a first target 30, which has the greatest amplitude at a frequency value of 50, and a second target 32, which is at a frequency value of approximately 75.
  • the real frequency components can be seen from the curve 22.
  • This spectrum is the resolution of the chirp versus the dimension of the distance.
  • a target can be determined by means of an amplitude maximum, the associated frequency value being able to be converted into the actual distance of the detection.
  • the distance is proportional to the measured frequency value. That means a higher frequency value corresponds to a greater distance.
  • the frequency values 50 and 75 correspond to the real frequency components of the time signal 10.
  • the spectrum 20, which results from the modified time signal 18, has 22 Störan parts 20a compared to the actual spectrum.
  • These interference components 20a are arranged around the real frequency component 30 and have a lower amplitude than this.
  • the amplitude of the interference components 20 a is higher than the amplitude of the real second frequency component 32.
  • the amplitude, frequency and phase of the real first frequency component 30 is now determined. This can be done, for example, by the methods already mentioned in the general description, whereby a precise determination is provided. After the real frequency has been determined using one of these methods, the amplitude and the phase can be determined using the single-point DFT using the interpolation method as follows:
  • the variables here are the sampling interval T s , the discrete signal x [k ⁇ with the k touch points.
  • the signal x [k] and the touch points can be multidimensional. This results in the frequency value S (/ 0 ) as a complex value with the
  • corresponds to the amplitude of the real frequency component.
  • the amplitude A 0 of the redemption spectrum can be derived from the first part of Formula 2
  • the zero-padding method or look-up tables are also available.
  • the characteristic quantities now known are used in a further step f in order to determine the modified redemption spectrum.
  • the repayment spectrum 28 is already known. Now the characteristic quantities are used to correct the amplitude of the redemption spectrum, to adapt its phase and to shift this to the target frequency / 0 , that is to say the first real frequency 30 here. This is shown graphically in FIG. 3, the transformation being illustrated by arrow 33. This results in the modified repayment spectrum 34.
  • the modified repayment spectrum 34 corresponds to the interference components which were introduced into the spectrum by the window function.
  • the modified redemption spectrum 34 is now deleted from the spectrum 20 in a step g.
  • the spectrum 20 is subtracted from the modified redemption spectrum 34, which results in a corrected spectrum 36.
  • the corrected spectrum around 36 is shown in FIG. 2 as a dashed line. Since the interference components are now removed, the second real frequency component 32 emerges from the spectrum. In comparison to spectrum 20, the second real frequency component can be identified. In addition, the interference components 36a of the second real frequency 32 can now be seen in FIG. 2.
  • the corrected spectrum 36 can be used according to a step h as the basis for a further correction.
  • the corrected spectrum 36 is accordingly used as the basis for step e.
  • step e.2 Further real frequency components are then determined in step e.2.
  • the already known real frequency components are no longer taken into account, since that Spectrum has already been adjusted for their interference components.
  • the newly determined real frequency components are evaluated in order to determine their characteristic quantities and then to determine one or more modified redemption spectra in a step f.2.
  • modified redemption spectra are then in step g.2. subtracted from the corrected spectrum on which step e was based in order to provide a corrected spectrum of the second stage.
  • steps can be repeated several times in order to provide an iterative improvement of the spectrum and to eliminate the corresponding interference components.
  • the steps are identified as e.x, f.x, g.x and h.x, where x stands for the number of the respective iterative step.
  • a step e it can be the case that only a single real frequency component is determined or several real frequency components are determined. In the latter case, interference components of different real frequency components can be canceled in a single pass.
  • the preceding description of the method relates, by way of example, to a RADAR system, in particular an FMCW radar, which is only resolved in one dimension, the distance.
  • the method can also be used in a higher dimensional space, which resolves among other things via distance, speed, elevation angle and / or azimuth angle. Accordingly, there are several time signals as well as multidimensional spectra, multidimensional corrected spectra, multidimensional repayment spectra and multidimensional modified repayment spectra.
  • the number of iterations can depend on various factors. For example, the number of iteration steps is specified, the number of targets to be determined or a certain limit.
  • a limit value can be used, all frequency components above the limit value being considered as real frequency components.
  • the limit becomes 5, 10, or 15, for example % set below a maximum amplitude of the spectrum.
  • the limit value can also be estimated using formula (5), which defines the maximum amplitude of the interference components in relation to the amplitude of the frequency components. The limit is lowered accordingly for each iteration step. All frequency components that are above the limit value are accordingly considered as real frequency components of the time signal.
  • the dimensions are usually determined one after the other by Fourier transformations.
  • the dimension of the distance is determined first, then the speed dimension and finally the angular dimensions by Fourier transformations.
  • the method can be used between each of the steps. For example, the method can be applied after the distance dimension has been resolved and the corrected spectra serve to resolve the further dimensions. However, the method can also be carried out after the provision of 2, 3 or after the fourth dimension has been made available.
  • the window function 38 represents the rectangular function.
  • the time or the tactile points are plotted against the X axis 40 and a factor that is between 1 and 0, for example, against the Y axis 42.
  • the factor determines the value by which the amplitude or the respective touch point is reduced.
  • the rectangular function corresponds to the width of the interference area 16 along the X axis 40 and thus includes the interference points.
  • a smoothed rectangular function 44 as shown in FIG. 4b, can also be used.
  • the transition can be carried out in different ways.
  • the cos 2 function was selected mathematically.
  • the transition from 1 to 0 can extend over a width of between 5 and 30 touch points.
  • touch points of the time signal 10 are reduced in amplitude, although no interference components 14 occur in this area.
  • this advantageously adapts the course of the redemption spectrum and its interference components.
  • a suitable window function By choosing a suitable window function, a lower amplitude of the interfering components can be achieved compared to the rectangular function, see formulas (2) and (5), and a faster drop with increasing relative frequency to the target frequency or the real frequency component. This makes the process more robust.
  • Step i is then carried out.
  • the characteristic quantities of the real frequency components ascertained such as frequency, phase and amplitude, are used to restore the erased interference areas.
  • the amplitude, frequency and phase are explicitly used and these are added in the form of sine functions.
  • the characteristic quantities of the real frequency components can be used after a first pass of step e.1 or steps e to h can be carried out several times in order to determine further real frequency components. After performing several iteration steps, the time signal can be reconstructed with high quality.
  • steps a to g according to FIG. 5 and the associated previous explanations executed and then determined the time signal from the corrected spectrum in a step j.
  • the corrected spectrum can be converted into the time signal, for example by an inverse Fourier transformation, the interference components having already been canceled.
  • the corrected spectrum it is possible for the corrected spectrum to take place after a first run through steps a to g or for steps e to g and h to run through several times accordingly.
  • Step h is run through once less than steps e to g. This will further improve the spectrum.
  • an efficient reconstruction of a time signal is possible.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

L'invention concerne un procédé de suppression d'interférences avec lequel : a. un signal de temps (10) est fourni, b. une plage d'interférences (16) du signal de temps (10) est déterminée, c. un signal de temps modifié (18) est fourni par effacement de la plage d'interférences (16) du signal de temps (18), d. un spectre (20) du signal de temps modifié (18) est déterminé et un spectre d'effacement (28) de la plage d'interférences effacée (16) est déterminé, e. une composante de fréquence réelle (30) du spectre (20) est déterminée, f. un spectre d'effacement modifié (34) est déterminé à partir d'une grandeur caractéristique de la composante de fréquence réelle (30) du spectre d'effacement (28), g. le spectre d'effacement modifié (34) est soustrait du spectre (20) afin de fournir un spectre corrigé (36). L'invention concerne en outre deux procédés de rétablissement du signal qui se basent sur le procédé de suppression d'interférences.
EP19817657.0A 2018-12-10 2019-12-06 Procédé de suppression d'interférences et procédés de rétablissement du signal Withdrawn EP3894881A1 (fr)

Applications Claiming Priority (2)

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DE102018221285.6A DE102018221285A1 (de) 2018-12-10 2018-12-10 Verfahren zur Interferenzunterdrückung und Verfahren zur Signalwiederherstellung
PCT/EP2019/084071 WO2020120333A1 (fr) 2018-12-10 2019-12-06 Procédé de suppression d'interférences et procédés de rétablissement du signal

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US (1) US20220120844A1 (fr)
EP (1) EP3894881A1 (fr)
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CN (1) CN113167856A (fr)
DE (1) DE102018221285A1 (fr)
WO (1) WO2020120333A1 (fr)

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DE102021210667A1 (de) 2021-09-24 2023-03-30 Zf Friedrichshafen Ag Vorverarbeitung eines Eingangssignals
JP2023165238A (ja) 2022-05-02 2023-11-15 株式会社デンソー レーダ装置

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EP1202468A3 (fr) * 2000-10-27 2004-01-14 Hitachi Kokusai Electric Inc. Dispositif de suppression de signaux d'interférence
JP4548954B2 (ja) * 2001-03-09 2010-09-22 株式会社日立国際電気 干渉信号除去装置
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JP2022510717A (ja) 2022-01-27
DE102018221285A1 (de) 2020-06-10
WO2020120333A1 (fr) 2020-06-18
US20220120844A1 (en) 2022-04-21

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